Multi-Objective Metamorphic Test Case Selection: an Industrial Case Study (Practical Experience Report)

Abstract

Metamorphic testing is a technique that has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of a system. However, this approach requires multiple test executions. In applications like Cyber-Physical Systems (CPSs), where the test executions can be very expensive in terms of time and resources needed, this can supose a problem. Therefore, it is paramount to optimize the test suite to reduce the costs of verifying the system. Test case selection is an optimization technique which accomplishes this by selecting a subset of test cases while aiming to preserve the effectiveness of the original test suite as much as possible. While there are many approaches for test case selection in the existing literature, none of them has been proposed for the metamorphic test case selection problem, where each metamorphic test case consists of a source and, at least, a follow-up test case pair. In this work, we present an evolutionary multi-objective approach for the metamorphic test case selection problem, adapting existing multi-objective test selection techniques and proposing new evolutionary operators and objective functions. Furthermore, we evaluate our approach with a set of metamorphic tests developed for an industrial case study from the elevation domain. The results suggest that our approach outperforms both Random Search and the same metaheuristic algorithm without the new evolutionary operators we propose.